iosr-JECE   Volume-1 ~ Issue-2

Paper Type	:	Research Paper
Title	:	Optimum Design of FIR Pulse Shaping Filter with Reduced Coefficients
Country	:	India
Authors	:	Rupesh Yadav, Puran Gour, Rajesh Nema

ABTRACTS : Distributed coding enables higher compression rate of a video signal. Till date we used a complex encoder and simple decoder. The advantage of having a simpler encoder and a complex decoder is increase in the compression ratio. Here in this project an optimized distributed video coding scheme is proposed which is a combination of an intraframe encoder and an interframe decoder. The distributed video coding scheme is based on punctured turbo codes. Turbo codes are punctured to generate scalable compressed video bit streams. An improved turbo coding performance leads to an overall performance improvement. Zero motion skip encoding strategy is used so that the decoder is made aware of these zero motion blocks such that encoding of these blocks can be avoided. The decoder may use this to improve the performance.
KEYWORDS :Distributed video coding, turbo codes, zero motion skip encoder strategy.

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ABSTRACT: wireless data acquisition, data logging and supervisory control are the basic building blocks of plant automation. In this paper plant consisting of multiple boilers where multiple process variables of the boilers need to be acquired from the field is considered and analysed. The data of the process variables needs to be logged in a database for further analysis and supervisory control. A Lab VIEW based wireless data logging and supervisory control program simulates the process and the generated data are logged in to the database with proper indication about the status of the process variable. This gives the overview of modern day data acquisition system, data loggers and supervisory control techniques. Wireless data acquisition and data logging program is used to log the measurements of different process variable data in a database. The database also shows the status of the process variable as normal or abnormal.
Keywords: Data acquisition,Data logging, , Lab view, Supervisorycontrol,Zigbee.

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Abstract: Image enhancement is a process which modifies the pixels of an image up to certain magnitude. It results in more subjectively pleasing image for human and machine analysis for some specific applications such as- X-Ray, fingerprinting, modelling photographs etc. It improves the visual appearance of pixels of an image. Pixels of an image can be manipulated in frequency as well as spatial domain. There are different image enhancement techniques comes under point processing in spatial domain such as- negative of an image, contrast stretching, thresholding, power law, logarithmic and grey level slicing.
Keywords: digital image processing, contrast stretching, grey level slicing, thresholding, image enhancement.

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ABSTRACT: Image segmentation is always a fundamental but challenging problem in computer vision. The simplest approach to image segmentation may be clustering of pixels. Our works in this paper address the problem of image segmentation under the paradigm of clustering. A robust clustering algorithm is proposed and utilized to do clustering on the L*a*b* color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. We test our segmentation method on medical images and Mat lab standard images.The experimental results clearly show region of interest object segmentation.
KEYWORDS: color space,L*a*b* color space, color image segmentation, color clustering technique, medical image segmentation

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ABSTRACT: One of the main challenges of computer vision is efficiently detecting and classifying objects in an image or video sequence. Several machine learning approaches have been applied to this problem, demonstrating significant improvements in detection accuracy and speed. However, most approaches have limited object detection to a single class of objects, such as faces or pedestrians. A common benchmark for computer vision researchers is face detection. Given a set of images, a face detection algorithm determines which images have sub-windows containing faces. This task is trivial for humans, but is computationally expensive for machines. Most face detection systems simplify the face detection problem by constraining the problem to frontal views of non-rotated faces. Approaches have been demonstrated capable of relaxing these constraints, at the cost of additional computation. They utilize AdaBoost to select a set of features and train a classifier. The detector uses a cascade structure to reduce the number of features considered for each sub-window. This approach is significantly faster than previous techniques and is applicable for real-time systems. There is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms
Keywords-AdaBoost, Correlator, Face Detection, FPGA,Template.

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ABSTRACT :This paper proposes the adaptation and optimization of two edge detector algorithms used for feature set extraction in CBIR. This paper compares the performance of Sobel and Canny edge detectors and proposed better solution for feature extraction in CBIR. It has been shown that the Canny edge detection algorithm performs better than Sobel edge detection with compromise of time. This work is implemented using MATLAB 7.10.0.

[1] Kunal J Pithadiya, Chintan K Modi, Jayesh D Chauhan, "Selecting the Most Favourable Edge Detection Technique for Liquid LevelInspection in Bottles" International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM) ISSN: 2150-7988 Vol.3 (2011), pp.034-044
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